【摘要】：近些年以來,中國、美國、印度等國家發(fā)生了多起大面積停電事故,造成了巨大的經(jīng)濟(jì)損失和社會影響�？稍偕茉�、電力負(fù)荷的進(jìn)一步增長、電力系統(tǒng)中電力電子化的廣泛應(yīng)用等增加了電網(wǎng)運(yùn)行的不確定性和復(fù)雜性,電網(wǎng)安全穩(wěn)定運(yùn)行面臨著嚴(yán)峻的挑戰(zhàn)。隨著人工智能、深度學(xué)習(xí)的興起,為研究人員從大數(shù)據(jù)、大樣本、概率學(xué)的角度分析電網(wǎng)信息物理系統(tǒng)的安全穩(wěn)定性,提供了堅實(shí)的理論基礎(chǔ)。本文采用深度卷積網(wǎng)絡(luò)(CNN)技術(shù),通過構(gòu)建可視化的全信息電網(wǎng)圖,將CNN方法引入到電力系統(tǒng)暫態(tài)安全穩(wěn)定性分析中。并在總結(jié)分析前人工作的基礎(chǔ)上,結(jié)合支路勢能的方法,解釋了系統(tǒng)暫態(tài)可視化過程中電網(wǎng)“撕裂”特征的機(jī)理,并提出快速判斷電力系統(tǒng)薄弱斷面以及暫態(tài)穩(wěn)定性快速判別的方法�；诳梢暬约袄碚摲治龅某晒�,構(gòu)建了深度卷積神經(jīng)網(wǎng)絡(luò)對系統(tǒng)暫態(tài)穩(wěn)定進(jìn)行分析,實(shí)現(xiàn)電力系統(tǒng)暫態(tài)穩(wěn)定快速評估。本文主要包括以下內(nèi)容:(1)構(gòu)建了動態(tài)可視化的電網(wǎng)圖。建立了基于電壓復(fù)平面的電力系統(tǒng)映射平面,以IEEE 10機(jī)39節(jié)點(diǎn)為例,獲取其仿真數(shù)據(jù)以及已有拓?fù)溥B接關(guān)系,基于Echart實(shí)現(xiàn)該系統(tǒng)節(jié)點(diǎn)信息在電壓復(fù)平面上動態(tài)展示。(2)建立了基于電壓復(fù)平面動態(tài)信息的電力系統(tǒng)暫態(tài)穩(wěn)定分析模型。從薄弱斷面的角度,分析了電壓相量距離和振蕩中心落點(diǎn)支路的關(guān)系,給出快速識別薄弱斷面的方法。在此基礎(chǔ)上,進(jìn)一步分析了薄弱斷面領(lǐng)先機(jī)群側(cè)母線的相軌跡特征,提出了基于薄弱斷面兩端母線相軌跡特征的暫態(tài)穩(wěn)定性判別方法。(3)開發(fā)了樣本批量制造程序,為深度學(xué)習(xí)提供足夠的樣本。借鑒深度學(xué)習(xí)方法在人工視覺領(lǐng)域處理數(shù)據(jù)的方法,以處理圖片像素矩陣的角度,將深度學(xué)習(xí)的方法應(yīng)用于電力系統(tǒng)暫態(tài)穩(wěn)定性評估。通過實(shí)驗的方法,確定了合適的模型參數(shù),提出了多窗口滑動識別電力系統(tǒng)暫態(tài)穩(wěn)定性的方式,減少了模型的參數(shù)并提升了判斷準(zhǔn)確率。
[Abstract]:In recent years, China, the United States, India and other countries have had a number of large-scale power outages, resulting in huge economic losses and social impact. With the further growth of renewable energy, power load and the wide application of electronization in power system, the uncertainty and complexity of power grid operation are increased, so the safe and stable operation of power network is facing severe challenges. With the rise of artificial intelligence and deep learning, it provides a solid theoretical basis for researchers to analyze the security and stability of power grid information physical systems from the perspective of big data, large samples and probabilities. In this paper, the deep convolution network (CNN) technique is used to construct a visual full information grid diagram, and the CNN method is introduced to the transient security stability analysis of power system. On the basis of summing up and analyzing the previous work, combined with the method of branch potential energy, the mechanism of power grid "tearing" in the process of system transient visualization is explained. A fast method for judging the weak section and transient stability of power system is proposed. Based on the results of visualization and theoretical analysis, a deep convolution neural network is constructed to analyze the transient stability of power system. The main contents of this paper are as follows: (1) A dynamic visual grid diagram is constructed. The power system mapping plane based on voltage complex plane is established. Taking 39 nodes of IEEE 10 machine as an example, the simulation data and the existing topology connection are obtained. The node information of the system is dynamically displayed on the voltage complex plane based on Echart. (2) the power system transient stability analysis model based on the voltage complex plane dynamic information is established. From the point of view of weak section, the relationship between the voltage phasor distance and the branch of the oscillation center drop point is analyzed, and the method to identify the weak section quickly is given. On this basis, the phase locus characteristics of the side bus of the weak section leading cluster are further analyzed, and a method of judging transient stability based on the phase trace characteristics of the weak section is proposed. (3) A batch manufacturing program is developed. Provide enough samples for deep learning. The depth learning method is applied to power system transient stability evaluation by using depth learning method for data processing in artificial vision field to deal with image pixel matrix. Through the experimental method, the suitable model parameters are determined, and a multi-window sliding identification method is proposed to identify the transient stability of power system, which reduces the parameters of the model and improves the accuracy of judgment.
【學(xué)位授予單位】：中國電力科學(xué)研究院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM712

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本文編號：2311991